serve.py 63.7 KB
Newer Older
1
# SPDX-License-Identifier: Apache-2.0
2
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3
4
5
6
r"""Benchmark online serving throughput.

On the server side, run one of the following commands
to launch the vLLM OpenAI API server:
7
    vllm serve <your_model> <engine arguments>
8
9
10

On the client side, run:
    vllm bench serve \
11
12
        --backend <backend or endpoint type. Default 'openai'> \
        --label <benchmark result label. Default using backend> \
13
        --model <your_model. Optional, defaults to first model from server> \
14
        --dataset-name <dataset_name. Default 'random'> \
15
16
        --input-len <general input length. Optional, maps to dataset-specific args> \
        --output-len <general output length. Optional, maps to dataset-specific args> \
17
18
19
        --request-rate <request_rate. Default inf> \
        --num-prompts <num_prompts. Default 1000>
"""
20

21
22
import argparse
import asyncio
23
import contextlib
24
import importlib.util
25
26
27
import json
import os
import random
28
import shutil
29
import time
30
import uuid
31
import warnings
32
from collections.abc import AsyncGenerator, Iterable
33
34
from dataclasses import dataclass
from datetime import datetime
35
from enum import Enum
36
from typing import Any, Literal
37

38
import aiohttp
39
40
41
import numpy as np
from tqdm.asyncio import tqdm

42
from vllm.benchmarks.datasets import SampleRequest, add_dataset_parser, get_samples
43
from vllm.benchmarks.lib.endpoint_request_func import (
44
45
46
47
48
    ASYNC_REQUEST_FUNCS,
    OPENAI_COMPATIBLE_BACKENDS,
    RequestFuncInput,
    RequestFuncOutput,
)
49
from vllm.benchmarks.lib.ready_checker import wait_for_endpoint
50
from vllm.benchmarks.lib.utils import convert_to_pytorch_benchmark_format, write_to_json
51
from vllm.tokenizers import TokenizerLike, get_tokenizer
52
from vllm.utils.gc_utils import freeze_gc_heap
53
from vllm.utils.network_utils import join_host_port
54
55
56

MILLISECONDS_TO_SECONDS_CONVERSION = 1000

57
58
59
TERM_PLOTLIB_AVAILABLE = (importlib.util.find_spec("termplotlib") is not None) and (
    shutil.which("gnuplot") is not None
)
60

61

62
63
async def get_first_model_from_server(
    base_url: str, headers: dict | None = None
64
) -> tuple[str, str]:
65
66
67
68
69
70
71
72
    """Fetch the first model from the server's /v1/models endpoint."""
    models_url = f"{base_url}/v1/models"
    async with aiohttp.ClientSession() as session:
        try:
            async with session.get(models_url, headers=headers) as response:
                response.raise_for_status()
                data = await response.json()
                if "data" in data and len(data["data"]) > 0:
73
                    return data["data"][0]["id"], data["data"][0]["root"]
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
                else:
                    raise ValueError(
                        f"No models found on the server at {base_url}. "
                        "Make sure the server is running and has models loaded."
                    )
        except (aiohttp.ClientError, json.JSONDecodeError) as e:
            raise RuntimeError(
                f"Failed to fetch models from server at {models_url}. "
                "Check that:\n"
                "1. The server is running\n"
                "2. The server URL is correct\n"
                f"Error: {e}"
            ) from e


89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
@dataclass
class SpecDecodeMetrics:
    """Speculative decoding metrics from the server's Prometheus endpoint."""

    num_drafts: int
    num_draft_tokens: int
    num_accepted_tokens: int
    accepted_per_pos: dict[int, int]


async def fetch_spec_decode_metrics(
    base_url: str, session: aiohttp.ClientSession
) -> SpecDecodeMetrics | None:
    """Fetch speculative decoding metrics from the server's Prometheus endpoint.

    Returns None if speculative decoding is not enabled or metrics are not available.
    """
    metrics_url = f"{base_url}/metrics"
    try:
        async with session.get(metrics_url) as response:
            if response.status != 200:
                return None
            text = await response.text()

            num_drafts = 0
            num_draft_tokens = 0
            num_accepted_tokens = 0
            accepted_per_pos: dict[int, int] = {}
            found_spec_decode = False

            for line in text.split("\n"):
                line = line.strip()
                if not line or line.startswith("#"):
                    continue

                if line.startswith("vllm:spec_decode"):
                    found_spec_decode = True
                    parts = line.split()
                    if parts:
                        with contextlib.suppress(ValueError):
                            if "num_drafts" in line:
                                num_drafts += int(float(parts[-1]))
                            elif "num_draft_tokens" in line:
                                num_draft_tokens += int(float(parts[-1]))
                            elif "num_accepted_tokens_per_pos" in line:
                                pos_label = 'position="'
                                if pos_label in line:
                                    start = line.index(pos_label) + len(pos_label)
                                    end = line.index('"', start)
                                    pos = int(line[start:end])
                                    val = int(float(parts[-1]))
                                    accepted_per_pos[pos] = (
                                        accepted_per_pos.get(pos, 0) + val
                                    )
                            elif "num_accepted_tokens" in line:
                                num_accepted_tokens += int(float(parts[-1]))

            if not found_spec_decode:
                return None

            return SpecDecodeMetrics(
                num_drafts=num_drafts,
                num_draft_tokens=num_draft_tokens,
                num_accepted_tokens=num_accepted_tokens,
                accepted_per_pos=accepted_per_pos,
            )
    except (aiohttp.ClientError, asyncio.TimeoutError):
        return None


159
160
class TaskType(Enum):
    GENERATION = "generation"
161
    POOLING = "pooling"
162
163


164
165
166
@dataclass
class BenchmarkMetrics:
    completed: int
167
    failed: int
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
    total_input: int
    total_output: int
    request_throughput: float
    request_goodput: float
    output_throughput: float
    total_token_throughput: float
    mean_ttft_ms: float
    median_ttft_ms: float
    std_ttft_ms: float
    percentiles_ttft_ms: list[tuple[float, float]]
    mean_tpot_ms: float
    median_tpot_ms: float
    std_tpot_ms: float
    percentiles_tpot_ms: list[tuple[float, float]]
    mean_itl_ms: float
    median_itl_ms: float
    std_itl_ms: float
    percentiles_itl_ms: list[tuple[float, float]]
    # E2EL stands for end-to-end latency per request.
    # It is the time taken on the client side from sending
    # a request to receiving a complete response.
    mean_e2el_ms: float
    median_e2el_ms: float
    std_e2el_ms: float
    percentiles_e2el_ms: list[tuple[float, float]]
193
194
195
196
    # Max output tokens per second and concurrent requests at that peak
    max_output_tokens_per_s: float
    max_concurrent_requests: int

197

198
199
200
@dataclass
class EmbedBenchmarkMetrics:
    completed: int
201
    failed: int
202
203
    total_input: int
    request_throughput: float
204
    total_token_throughput: float
205
206
207
208
    mean_e2el_ms: float
    std_e2el_ms: float
    median_e2el_ms: float
    percentiles_e2el_ms: float
209

210

211
def _get_current_request_rate(
212
213
214
    ramp_up_strategy: Literal["linear", "exponential"] | None,
    ramp_up_start_rps: int | None,
    ramp_up_end_rps: int | None,
215
216
217
218
    request_index: int,
    total_requests: int,
    request_rate: float,
) -> float:
219
220
221
222
223
    if (
        ramp_up_strategy
        and ramp_up_start_rps is not None
        and ramp_up_end_rps is not None
    ):
224
225
226
227
228
229
230
231
232
233
234
235
        progress = request_index / max(total_requests - 1, 1)
        if ramp_up_strategy == "linear":
            increase = (ramp_up_end_rps - ramp_up_start_rps) * progress
            return ramp_up_start_rps + increase
        elif ramp_up_strategy == "exponential":
            ratio = ramp_up_end_rps / ramp_up_start_rps
            return ramp_up_start_rps * (ratio**progress)
        else:
            raise ValueError(f"Unknown ramp-up strategy: {ramp_up_strategy}")
    return request_rate


236
async def get_request(
237
    input_requests: list[SampleRequest],
238
239
    request_rate: float,
    burstiness: float = 1.0,
240
241
242
    ramp_up_strategy: Literal["linear", "exponential"] | None = None,
    ramp_up_start_rps: int | None = None,
    ramp_up_end_rps: int | None = None,
243
) -> AsyncGenerator[tuple[SampleRequest, float], None]:
244
245
    """
    Asynchronously generates requests at a specified rate
246
    with OPTIONAL burstiness and OPTIONAL ramp-up strategy.
247
248
249

    Args:
        input_requests:
250
            A list of input requests, each represented as a SampleRequest.
251
252
253
254
255
256
257
258
259
260
        request_rate:
            The rate at which requests are generated (requests/s).
        burstiness (optional):
            The burstiness factor of the request generation.
            Only takes effect when request_rate is not inf.
            Default value is 1, which follows a Poisson process.
            Otherwise, the request intervals follow a gamma distribution.
            A lower burstiness value (0 < burstiness < 1) results
            in more bursty requests, while a higher burstiness value
            (burstiness > 1) results in a more uniform arrival of requests.
261
        ramp_up_strategy (optional):
262
263
264
265
266
267
            The ramp-up strategy. Can be "linear" or "exponential".
            If None, uses constant request rate (specified by request_rate).
        ramp_up_start_rps (optional):
            The starting request rate for ramp-up.
        ramp_up_end_rps (optional):
            The ending request rate for ramp-up.
268
269
    """
    assert burstiness > 0, (
270
271
        f"A positive burstiness factor is expected, but given {burstiness}."
    )
272
    # Convert to list to get length for ramp-up calculations
273
    if isinstance(input_requests, Iterable) and not isinstance(input_requests, list):
274
        input_requests = list(input_requests)
275

276
    total_requests = len(input_requests)
277
    assert total_requests > 0, "No requests provided."
278

279
280
281
282
    # Precompute delays among requests to minimize request send laggings
    request_rates = []
    delay_ts = []
    for request_index, request in enumerate(input_requests):
283
        current_request_rate = _get_current_request_rate(
284
285
286
287
288
289
290
            ramp_up_strategy,
            ramp_up_start_rps,
            ramp_up_end_rps,
            request_index,
            total_requests,
            request_rate,
        )
291
292
293
        assert current_request_rate > 0.0, (
            f"Obtained non-positive request rate {current_request_rate}."
        )
294
        request_rates.append(current_request_rate)
295
        if current_request_rate == float("inf"):
296
            delay_ts.append(0)
297
298
299
300
        elif burstiness == float("inf"):
            # when burstiness tends to infinity, the delay time becomes constant
            # and tends to the inverse of the request rate
            delay_ts.append(1.0 / current_request_rate)
301
302
303
304
305
306
        else:
            theta = 1.0 / (current_request_rate * burstiness)

            # Sample the request interval from the gamma distribution.
            # If burstiness is 1, it follows exponential distribution.
            delay_ts.append(np.random.gamma(shape=burstiness, scale=theta))
307

308
309
310
311
312
313
314
315
316
    # Calculate the cumulative delay time from the first sent out requests.
    for i in range(1, len(delay_ts)):
        delay_ts[i] += delay_ts[i - 1]
    if ramp_up_strategy is None and delay_ts[-1] != 0:
        # When ramp_up_strategy is not set, we assume the request rate is fixed
        # and all requests should be sent in target_total_delay_s, the following
        # logic would re-scale delay time to ensure the final delay_ts
        # align with target_total_delay_s.
        #
317
318
        # NOTE: If we simply accumulate the random delta values
        # from the gamma distribution, their sum would have 1-2% gap
319
        # from target_total_delay_s. The purpose of the following logic is to
co63oc's avatar
co63oc committed
320
        # close the gap for stabilizing the throughput data
321
        # from different random seeds.
322
323
324
325
326
327
        target_total_delay_s = total_requests / request_rate
        normalize_factor = target_total_delay_s / delay_ts[-1]
        delay_ts = [delay * normalize_factor for delay in delay_ts]

    start_ts = time.time()
    for request_index, request in enumerate(input_requests):
328
329
330
331
332
        if delay_ts[request_index] > 0:
            current_ts = time.time()
            sleep_interval_s = start_ts + delay_ts[request_index] - current_ts
            if sleep_interval_s > 0:
                await asyncio.sleep(sleep_interval_s)
333
        yield request, request_rates[request_index]
334
335


336
def calculate_metrics_for_embeddings(
337
338
339
    outputs: list[RequestFuncOutput],
    dur_s: float,
    selected_percentiles: list[float],
340
) -> EmbedBenchmarkMetrics:
341
342
343
344
345
346
347
348
349
350
351
352
    """Calculate the metrics for the embedding requests.

    Args:
        outputs: The outputs of the requests.
        dur_s: The duration of the benchmark.
        selected_percentiles: The percentiles to select.

    Returns:
        The calculated benchmark metrics.
    """
    total_input = 0
    completed = 0
353
    failed = 0
354
355
356
357
358
359
    e2els: list[float] = []
    for i in range(len(outputs)):
        if outputs[i].success:
            e2els.append(outputs[i].latency)
            completed += 1
            total_input += outputs[i].prompt_len
360
361
        else:
            failed += 1
362
363
364
365
366

    if completed == 0:
        warnings.warn(
            "All requests failed. This is likely due to a misconfiguration "
            "on the benchmark arguments.",
367
368
            stacklevel=2,
        )
369
370
    metrics = EmbedBenchmarkMetrics(
        completed=completed,
371
        failed=failed,
372
373
374
375
376
377
        total_input=total_input,
        request_throughput=completed / dur_s,
        total_token_throughput=total_input / dur_s,
        mean_e2el_ms=np.mean(e2els or 0) * 1000,
        std_e2el_ms=np.std(e2els or 0) * 1000,
        median_e2el_ms=np.median(e2els or 0) * 1000,
378
379
380
        percentiles_e2el_ms=[
            (p, np.percentile(e2els or 0, p) * 1000) for p in selected_percentiles
        ],
381
382
383
384
    )
    return metrics


385
def calculate_metrics(
386
    input_requests: list[SampleRequest],
387
388
    outputs: list[RequestFuncOutput],
    dur_s: float,
389
    tokenizer: TokenizerLike,
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
    selected_percentiles: list[float],
    goodput_config_dict: dict[str, float],
) -> tuple[BenchmarkMetrics, list[int]]:
    """Calculate the metrics for the benchmark.

    Args:
        input_requests: The input requests.
        outputs: The outputs of the requests.
        dur_s: The duration of the benchmark.
        tokenizer: The tokenizer to use.
        selected_percentiles: The percentiles to select.
        goodput_config_dict: The goodput configuration.

    Returns:
        A tuple of the benchmark metrics and the actual output lengths.
    """
    actual_output_lens: list[int] = []
    total_input = 0
    completed = 0
    good_completed = 0
    itls: list[float] = []
    tpots: list[float] = []
    all_tpots: list[float] = []
    ttfts: list[float] = []
    e2els: list[float] = []
    for i in range(len(outputs)):
        if outputs[i].success:
            output_len = outputs[i].output_tokens

419
            if not output_len:
420
421
422
423
424
425
                # We use the tokenizer to count the number of output tokens
                # for some serving backends instead of looking at
                # len(outputs[i].itl) since multiple output tokens may be
                # bundled together
                # Note : this may inflate the output token count slightly
                output_len = len(
426
427
428
429
                    tokenizer(
                        outputs[i].generated_text, add_special_tokens=False
                    ).input_ids
                )
430
            actual_output_lens.append(output_len)
431
            total_input += input_requests[i].prompt_len
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
            tpot = 0
            if output_len > 1:
                latency_minus_ttft = outputs[i].latency - outputs[i].ttft
                tpot = latency_minus_ttft / (output_len - 1)
                tpots.append(tpot)
            # Note: if output_len <= 1, we regard tpot as 0 for goodput
            all_tpots.append(tpot)
            itls += outputs[i].itl
            ttfts.append(outputs[i].ttft)
            e2els.append(outputs[i].latency)
            completed += 1
        else:
            actual_output_lens.append(0)

    if goodput_config_dict:
        valid_metrics = []
        slo_values = []

        if "ttft" in goodput_config_dict:
            valid_metrics.append(ttfts)
452
453
454
            slo_values.append(
                goodput_config_dict["ttft"] / MILLISECONDS_TO_SECONDS_CONVERSION
            )
455
456
        if "tpot" in goodput_config_dict:
            valid_metrics.append(all_tpots)
457
458
459
            slo_values.append(
                goodput_config_dict["tpot"] / MILLISECONDS_TO_SECONDS_CONVERSION
            )
460
461
        if "e2el" in goodput_config_dict:
            valid_metrics.append(e2els)
462
463
464
            slo_values.append(
                goodput_config_dict["e2el"] / MILLISECONDS_TO_SECONDS_CONVERSION
            )
465
466
467
468
469
470
471
472
473
474

        for req_metric in zip(*valid_metrics):
            is_good_req = all([s >= r for s, r in zip(slo_values, req_metric)])
            if is_good_req:
                good_completed += 1

    if completed == 0:
        warnings.warn(
            "All requests failed. This is likely due to a misconfiguration "
            "on the benchmark arguments.",
475
476
            stacklevel=2,
        )
477
478
479
480
481
482
483

    # Calculate max output tokens per second metric
    max_output_tokens_per_s = 0.0
    max_concurrent_requests = 0

    # Find the time range across all successful requests
    successful_outputs = [output for output in outputs if output.success]
484
    failed_outputs = [output for output in outputs if not output.success]
485
486
487
488
489
490

    if len(failed_outputs) > 0:
        print("Failed requests during benchmark run detected (capping to 10):")
        for i, err in enumerate(failed_outputs[:10]):
            print(f"Error {i}: {err.error}")

491
    if successful_outputs:
492
493
494
495
        min_start_time = min(output.start_time for output in successful_outputs)
        max_end_time = max(
            output.start_time + output.latency for output in successful_outputs
        )
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518

        # Create second buckets (ceiling to ensure we capture all time)
        duration_seconds = int(np.ceil(max_end_time - min_start_time)) + 1
        tokens_per_second = np.zeros(duration_seconds)
        concurrent_requests_per_second = np.zeros(duration_seconds)

        for i, output in enumerate(successful_outputs):
            # Calculate token generation timestamp using
            # start_time, ttft, and itl
            token_times = [output.start_time + output.ttft]
            current_time = token_times[0]
            for itl_value in output.itl:
                current_time += itl_value
                token_times.append(current_time)

            # Add tokens to second buckets
            for token_time in token_times:
                second_bucket = int(token_time - min_start_time)
                if 0 <= second_bucket < duration_seconds:
                    tokens_per_second[second_bucket] += 1

            # Track concurrent requests for each second this request was active
            request_start_second = int(output.start_time - min_start_time)
519
520
521
            request_end_second = int(
                (output.start_time + output.latency) - min_start_time
            )
522
523
524
525
526
527
528
529

            for second in range(request_start_second, request_end_second + 1):
                concurrent_requests_per_second[second] += 1

        # Find the maximum tokens per second and corresponding
        # concurrent requests
        if len(tokens_per_second) > 0:
            max_output_tokens_per_s = float(np.max(tokens_per_second))
530
            max_concurrent_requests = int(np.max(concurrent_requests_per_second))
531
532
533

        if TERM_PLOTLIB_AVAILABLE:
            import termplotlib as tpl
534

535
            fig = tpl.figure()
536
537
538
539
540
541
542
543
544
545
            fig.plot(
                np.arange(len(tokens_per_second)),
                tokens_per_second,
                title="Output tokens per second",
            )
            fig.plot(
                np.arange(len(concurrent_requests_per_second)),
                concurrent_requests_per_second,
                title="Concurrent requests per second",
            )
546
547
548
549
            fig.show()
        else:
            print("tip: install termplotlib and gnuplot to plot the metrics")

550
551
    metrics = BenchmarkMetrics(
        completed=completed,
552
        failed=len(failed_outputs),
553
554
555
556
557
558
        total_input=total_input,
        total_output=sum(actual_output_lens),
        request_throughput=completed / dur_s,
        request_goodput=good_completed / dur_s,
        output_throughput=sum(actual_output_lens) / dur_s,
        total_token_throughput=(total_input + sum(actual_output_lens)) / dur_s,
559
560
        mean_ttft_ms=np.mean(ttfts or 0)
        * 1000,  # ttfts is empty if streaming is not supported by the endpoint
561
562
        std_ttft_ms=np.std(ttfts or 0) * 1000,
        median_ttft_ms=np.median(ttfts or 0) * 1000,
563
564
565
        percentiles_ttft_ms=[
            (p, np.percentile(ttfts or 0, p) * 1000) for p in selected_percentiles
        ],
566
567
568
        mean_tpot_ms=np.mean(tpots or 0) * 1000,
        std_tpot_ms=np.std(tpots or 0) * 1000,
        median_tpot_ms=np.median(tpots or 0) * 1000,
569
570
571
        percentiles_tpot_ms=[
            (p, np.percentile(tpots or 0, p) * 1000) for p in selected_percentiles
        ],
572
573
574
        mean_itl_ms=np.mean(itls or 0) * 1000,
        std_itl_ms=np.std(itls or 0) * 1000,
        median_itl_ms=np.median(itls or 0) * 1000,
575
576
577
        percentiles_itl_ms=[
            (p, np.percentile(itls or 0, p) * 1000) for p in selected_percentiles
        ],
578
579
580
        mean_e2el_ms=np.mean(e2els or 0) * 1000,
        std_e2el_ms=np.std(e2els or 0) * 1000,
        median_e2el_ms=np.median(e2els or 0) * 1000,
581
582
583
        percentiles_e2el_ms=[
            (p, np.percentile(e2els or 0, p) * 1000) for p in selected_percentiles
        ],
584
585
        max_output_tokens_per_s=max_output_tokens_per_s,
        max_concurrent_requests=max_concurrent_requests,
586
587
588
589
590
591
    )

    return metrics, actual_output_lens


async def benchmark(
592
    task_type: TaskType,
593
594
595
596
597
    endpoint_type: str,
    api_url: str,
    base_url: str,
    model_id: str,
    model_name: str,
598
    tokenizer: TokenizerLike,
599
    input_requests: list[SampleRequest],
600
    logprobs: int | None,
601
602
603
    request_rate: float,
    burstiness: float,
    disable_tqdm: bool,
604
    num_warmups: int,
605
606
    profile: bool,
    selected_percentile_metrics: list[str],
607
    selected_percentiles: list[float],
608
609
    ignore_eos: bool,
    goodput_config_dict: dict[str, float],
610
611
612
613
614
615
616
    max_concurrency: int | None,
    lora_modules: Iterable[str] | None,
    extra_headers: dict | None,
    extra_body: dict | None,
    ramp_up_strategy: Literal["linear", "exponential"] | None = None,
    ramp_up_start_rps: int | None = None,
    ramp_up_end_rps: int | None = None,
617
    ready_check_timeout_sec: int = 600,
618
):
619
620
621
622
    try:
        request_func = ASYNC_REQUEST_FUNCS[endpoint_type]
    except KeyError:
        raise ValueError(f"Unknown backend: {endpoint_type}") from None
623

624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
    # Reuses connections across requests to reduce TLS handshake overhead.
    connector = aiohttp.TCPConnector(
        limit=max_concurrency or 0,
        limit_per_host=max_concurrency or 0,
        ttl_dns_cache=300,
        use_dns_cache=True,
        keepalive_timeout=60,
        enable_cleanup_closed=True,
        force_close=False,
        ssl=("https://" in api_url),
    )

    session = aiohttp.ClientSession(
        connector=connector,
        trust_env=True,
        timeout=aiohttp.ClientTimeout(total=6 * 60 * 60),
    )

642
643
    print("Starting initial single prompt test run...")
    test_prompt, test_prompt_len, test_output_len, test_mm_content = (
644
645
646
647
648
649
        input_requests[0].prompt,
        input_requests[0].prompt_len,
        input_requests[0].expected_output_len,
        input_requests[0].multi_modal_data,
    )

650
651
652
653
654
655
656
657
    assert (
        test_mm_content is None
        or isinstance(test_mm_content, dict)
        or (
            isinstance(test_mm_content, list)
            and all(isinstance(item, dict) for item in test_mm_content)
        )
    ), "multi_modal_data must be a dict or list[dict]"
658
659
660
661
662
663
664
665
666
667
    test_input = RequestFuncInput(
        model=model_id,
        model_name=model_name,
        prompt=test_prompt,
        api_url=api_url,
        prompt_len=test_prompt_len,
        output_len=test_output_len,
        logprobs=logprobs,
        multi_modal_content=test_mm_content,
        ignore_eos=ignore_eos,
668
        extra_headers=extra_headers,
669
        extra_body=extra_body,
670
671
    )

672
673
674
675
676
677
678
679
680
681
682
    if ready_check_timeout_sec > 0:
        test_output = await wait_for_endpoint(
            request_func,
            test_input,
            session,
            timeout_seconds=ready_check_timeout_sec,
        )
        if not test_output.success:
            raise ValueError(
                "Initial test run failed - Please make sure benchmark "
                "arguments are correctly specified. "
683
684
                f"Error: {test_output.error}"
            )
685
        else:
686
            print("Initial test run completed.")
687
    else:
688
        print("Skipping endpoint ready check.")
689

690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
    if num_warmups > 0:
        print(f"Warming up with {num_warmups} requests...")
        warmup_pbar = None if disable_tqdm else tqdm(total=num_warmups)
        warmup_semaphore = (
            asyncio.Semaphore(max_concurrency)
            if max_concurrency
            else contextlib.nullcontext()
        )
        warmup_tasks = []

        async def warmup_limited_request_func():
            async with warmup_semaphore:
                return await request_func(
                    request_func_input=test_input, session=session, pbar=warmup_pbar
                )

        for _ in range(num_warmups):
            request_task = asyncio.create_task(warmup_limited_request_func())
            warmup_tasks.append(request_task)
        _ = await asyncio.gather(*warmup_tasks)

        if warmup_pbar is not None:
            warmup_pbar.close()
        print("Warmup run completed.")

    print("Starting main benchmark run...")

717
718
719
    if lora_modules:
        # For each input request, choose a LoRA module at random.
        lora_modules = iter(
720
721
            [random.choice(lora_modules) for _ in range(len(input_requests))]
        )
722
723
724

    if profile:
        print("Starting profiler...")
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
        profile_input = RequestFuncInput(
            model=model_id,
            model_name=model_name,
            prompt=test_prompt,
            api_url=base_url + "/start_profile",
            prompt_len=test_prompt_len,
            output_len=test_output_len,
            logprobs=logprobs,
            multi_modal_content=test_mm_content,
            ignore_eos=ignore_eos,
            extra_headers=extra_headers,
            extra_body=extra_body,
        )
        profile_output = await request_func(
            request_func_input=profile_input, session=session
        )
741
742
743
        if profile_output.success:
            print("Profiler started")

744
    distribution = "Poisson process" if burstiness == 1.0 else "Gamma distribution"
745
746
747

    if ramp_up_strategy is not None:
        print(f"Traffic ramp-up strategy: {ramp_up_strategy}.")
748
749
750
751
        print(
            f"Will increase RPS from {ramp_up_start_rps} to "
            f"{ramp_up_end_rps} RPS over the duration of the benchmark."
        )
752
    else:
753
        print(f"Traffic request rate: {request_rate}")
754
755
756
757

    print(f"Burstiness factor: {burstiness} ({distribution})")
    print(f"Maximum request concurrency: {max_concurrency}")

758
759
    spec_decode_metrics_before = await fetch_spec_decode_metrics(base_url, session)

760
761
    pbar = None if disable_tqdm else tqdm(total=len(input_requests))

762
763
764
765
766
    semaphore = (
        asyncio.Semaphore(max_concurrency)
        if max_concurrency
        else contextlib.nullcontext()
    )
767

768
    async def limited_request_func(request_func_input, session, pbar):
769
        async with semaphore:
770
771
772
            return await request_func(
                request_func_input=request_func_input, session=session, pbar=pbar
            )
773
774
775

    benchmark_start_time = time.perf_counter()
    tasks: list[asyncio.Task] = []
776
777
778
779
780

    rps_change_events = []
    last_int_rps = -1
    if ramp_up_strategy is not None and ramp_up_start_rps is not None:
        last_int_rps = ramp_up_start_rps
781
782
783
784
785
786
        rps_change_events.append(
            {
                "rps": last_int_rps,
                "timestamp": datetime.now().isoformat(),
            }
        )
787
788

    async for request, current_request_rate in get_request(
789
790
791
792
793
794
795
        input_requests,
        request_rate,
        burstiness,
        ramp_up_strategy,
        ramp_up_start_rps,
        ramp_up_end_rps,
    ):
796
797
798
799
800
        if ramp_up_strategy is not None:
            current_int_rps = int(current_request_rate)
            if current_int_rps > last_int_rps:
                timestamp = datetime.now().isoformat()
                for rps_val in range(last_int_rps + 1, current_int_rps + 1):
801
                    rps_change_events.append({"rps": rps_val, "timestamp": timestamp})
802
                last_int_rps = current_int_rps
803
        prompt, prompt_len, output_len, mm_content, request_id = (
804
805
806
807
            request.prompt,
            request.prompt_len,
            request.expected_output_len,
            request.multi_modal_data,
808
            request.request_id,
809
        )
810
811
812
813
814
        req_model_id, req_model_name = model_id, model_name
        if lora_modules:
            req_lora_module = next(lora_modules)
            req_model_id, req_model_name = req_lora_module, req_lora_module

815
816
817
818
819
820
821
822
823
824
825
826
827
828
        request_func_input = RequestFuncInput(
            model=req_model_id,
            model_name=req_model_name,
            prompt=prompt,
            api_url=api_url,
            prompt_len=prompt_len,
            output_len=output_len,
            logprobs=logprobs,
            multi_modal_content=mm_content,
            ignore_eos=ignore_eos,
            extra_headers=extra_headers,
            extra_body=extra_body,
            request_id=request_id,
        )
829
830
        tasks.append(
            asyncio.create_task(
831
832
833
834
835
                limited_request_func(
                    request_func_input=request_func_input, session=session, pbar=pbar
                )
            )
        )
836
837
838
839
840
841
842
    outputs: list[RequestFuncOutput] = await asyncio.gather(*tasks)

    if pbar is not None:
        pbar.close()

    benchmark_duration = time.perf_counter() - benchmark_start_time

843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
    spec_decode_metrics_after = await fetch_spec_decode_metrics(base_url, session)
    spec_decode_stats: dict[str, Any] | None = None
    if spec_decode_metrics_before is not None and spec_decode_metrics_after is not None:
        delta_drafts = (
            spec_decode_metrics_after.num_drafts - spec_decode_metrics_before.num_drafts
        )
        delta_draft_tokens = (
            spec_decode_metrics_after.num_draft_tokens
            - spec_decode_metrics_before.num_draft_tokens
        )
        delta_accepted = (
            spec_decode_metrics_after.num_accepted_tokens
            - spec_decode_metrics_before.num_accepted_tokens
        )
        per_pos_rates: list[float] = []
        if delta_drafts > 0:
            positions = sorted(
                set(spec_decode_metrics_before.accepted_per_pos.keys())
                | set(spec_decode_metrics_after.accepted_per_pos.keys())
            )
            for pos in positions:
                before_val = spec_decode_metrics_before.accepted_per_pos.get(pos, 0)
                after_val = spec_decode_metrics_after.accepted_per_pos.get(
                    pos, before_val
                )
                delta_pos = after_val - before_val
                per_pos_rates.append(delta_pos / delta_drafts)

        if delta_draft_tokens > 0:
            acceptance_rate = (delta_accepted / delta_draft_tokens) * 100
            acceptance_length = (
                1 + delta_accepted / delta_drafts if delta_drafts > 0 else 0.0
            )
            spec_decode_stats = {
                "num_drafts": delta_drafts,
                "draft_tokens": delta_draft_tokens,
                "accepted_tokens": delta_accepted,
                "acceptance_rate": acceptance_rate,
                "acceptance_length": acceptance_length,
                "per_position_acceptance_rates": per_pos_rates,
            }

885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
    if task_type == TaskType.GENERATION:
        metrics, actual_output_lens = calculate_metrics(
            input_requests=input_requests,
            outputs=outputs,
            dur_s=benchmark_duration,
            tokenizer=tokenizer,
            selected_percentiles=selected_percentiles,
            goodput_config_dict=goodput_config_dict,
        )
    else:
        metrics = calculate_metrics_for_embeddings(
            outputs=outputs,
            dur_s=benchmark_duration,
            selected_percentiles=selected_percentiles,
        )
        actual_output_lens = 0
901

902
    print("{s:{c}^{n}}".format(s=" Serving Benchmark Result ", n=50, c="="))
903
    print("{:<40} {:<10}".format("Successful requests:", metrics.completed))
904
    print("{:<40} {:<10}".format("Failed requests:", metrics.failed))
905
    if max_concurrency is not None:
906
907
908
909
        print("{:<40} {:<10}".format("Maximum request concurrency:", max_concurrency))
    if request_rate != float("inf"):
        print("{:<40} {:<10.2f}".format("Request rate configured (RPS):", request_rate))
    print("{:<40} {:<10.2f}".format("Benchmark duration (s):", benchmark_duration))
910
    print("{:<40} {:<10}".format("Total input tokens:", metrics.total_input))
911
    if isinstance(metrics, BenchmarkMetrics):
912
913
914
915
916
917
        print("{:<40} {:<10}".format("Total generated tokens:", metrics.total_output))
    print(
        "{:<40} {:<10.2f}".format(
            "Request throughput (req/s):", metrics.request_throughput
        )
    )
918
    if goodput_config_dict:
919
920
921
922
923
        print(
            "{:<40} {:<10.2f}".format(
                "Request goodput (req/s):", metrics.request_goodput
            )
        )
924
    if isinstance(metrics, BenchmarkMetrics):
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
        print(
            "{:<40} {:<10.2f}".format(
                "Output token throughput (tok/s):", metrics.output_throughput
            )
        )
        print(
            "{:<40} {:<10.2f}".format(
                "Peak output token throughput (tok/s):", metrics.max_output_tokens_per_s
            )
        )
        print(
            "{:<40} {:<10.2f}".format(
                "Peak concurrent requests:", metrics.max_concurrent_requests
            )
        )
    print(
        "{:<40} {:<10.2f}".format(
942
            "Total token throughput (tok/s):", metrics.total_token_throughput
943
944
        )
    )
945

946
947
948
949
    if isinstance(metrics, BenchmarkMetrics):
        result = {
            "duration": benchmark_duration,
            "completed": metrics.completed,
950
            "failed": metrics.failed,
951
952
953
            "total_input_tokens": metrics.total_input,
            "total_output_tokens": metrics.total_output,
            "request_throughput": metrics.request_throughput,
954
            "request_goodput": metrics.request_goodput if goodput_config_dict else None,
955
956
957
958
959
960
961
962
            "output_throughput": metrics.output_throughput,
            "total_token_throughput": metrics.total_token_throughput,
            "input_lens": [output.prompt_len for output in outputs],
            "output_lens": actual_output_lens,
            "ttfts": [output.ttft for output in outputs],
            "itls": [output.itl for output in outputs],
            "generated_texts": [output.generated_text for output in outputs],
            "errors": [output.error for output in outputs],
963
964
            "max_output_tokens_per_s": metrics.max_output_tokens_per_s,
            "max_concurrent_requests": metrics.max_concurrent_requests,
965
966
967
968
969
970
971
972
973
974
975
        }
    else:
        result = {
            "duration": benchmark_duration,
            "completed": metrics.completed,
            "total_input_tokens": metrics.total_input,
            "request_throughput": metrics.request_throughput,
            "total_token_throughput": metrics.total_token_throughput,
            "input_lens": [output.prompt_len for output in outputs],
            "errors": [output.error for output in outputs],
        }
976

977
978
979
    if rps_change_events:
        result["rps_change_events"] = rps_change_events

980
981
982
983
984
985
986
987
988
989
990
991
    if spec_decode_stats is not None:
        result["spec_decode_acceptance_rate"] = spec_decode_stats["acceptance_rate"]
        result["spec_decode_acceptance_length"] = spec_decode_stats["acceptance_length"]
        result["spec_decode_num_drafts"] = int(spec_decode_stats["num_drafts"])
        result["spec_decode_draft_tokens"] = int(spec_decode_stats["draft_tokens"])
        result["spec_decode_accepted_tokens"] = int(
            spec_decode_stats["accepted_tokens"]
        )
        result["spec_decode_per_position_acceptance_rates"] = spec_decode_stats.get(
            "per_position_acceptance_rates", []
        )

992
993
994
995
996
997
998
999
1000
1001
1002
1003
    def process_one_metric(
        # E.g., "ttft"
        metric_attribute_name: str,
        # E.g., "TTFT"
        metric_name: str,
        # E.g., "Time to First Token"
        metric_header: str,
    ):
        # This function prints and adds statistics of the specified
        # metric.
        if metric_attribute_name not in selected_percentile_metrics:
            return
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
        print("{s:{c}^{n}}".format(s=metric_header, n=50, c="-"))
        print(
            "{:<40} {:<10.2f}".format(
                f"Mean {metric_name} (ms):",
                getattr(metrics, f"mean_{metric_attribute_name}_ms"),
            )
        )
        print(
            "{:<40} {:<10.2f}".format(
                f"Median {metric_name} (ms):",
                getattr(metrics, f"median_{metric_attribute_name}_ms"),
            )
        )
1017
        result[f"mean_{metric_attribute_name}_ms"] = getattr(
1018
1019
            metrics, f"mean_{metric_attribute_name}_ms"
        )
1020
        result[f"median_{metric_attribute_name}_ms"] = getattr(
1021
1022
            metrics, f"median_{metric_attribute_name}_ms"
        )
1023
        result[f"std_{metric_attribute_name}_ms"] = getattr(
1024
1025
1026
            metrics, f"std_{metric_attribute_name}_ms"
        )
        for p, value in getattr(metrics, f"percentiles_{metric_attribute_name}_ms"):
1027
            p_word = str(int(p)) if int(p) == p else str(p)
1028
            print("{:<40} {:<10.2f}".format(f"P{p_word} {metric_name} (ms):", value))
1029
1030
            result[f"p{p_word}_{metric_attribute_name}_ms"] = value

1031
1032
    if task_type == TaskType.GENERATION:
        process_one_metric("ttft", "TTFT", "Time to First Token")
1033
        process_one_metric("tpot", "TPOT", "Time per Output Token (excl. 1st token)")
1034
        process_one_metric("itl", "ITL", "Inter-token Latency")
1035
1036
    process_one_metric("e2el", "E2EL", "End-to-end Latency")

1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
    if spec_decode_stats is not None:
        print("{s:{c}^{n}}".format(s="Speculative Decoding", n=50, c="-"))
        print(
            "{:<40} {:<10.2f}".format(
                "Acceptance rate (%):", spec_decode_stats["acceptance_rate"]
            )
        )
        print(
            "{:<40} {:<10.2f}".format(
                "Acceptance length:", spec_decode_stats["acceptance_length"]
            )
        )
        print("{:<40} {:<10}".format("Drafts:", int(spec_decode_stats["num_drafts"])))
        print(
            "{:<40} {:<10}".format(
                "Draft tokens:", int(spec_decode_stats["draft_tokens"])
            )
        )
        print(
            "{:<40} {:<10}".format(
                "Accepted tokens:", int(spec_decode_stats["accepted_tokens"])
            )
        )
        per_pos = spec_decode_stats.get("per_position_acceptance_rates", [])
        if per_pos:
            print("Per-position acceptance (%):")
            for i, rate in enumerate(per_pos):
                print("{:<40} {:<10.2f}".format(f"  Position {i}:", rate * 100))

1066
1067
    print("=" * 50)

1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
    if profile:
        print("Stopping profiler...")
        profile_input = RequestFuncInput(
            model=model_id,
            prompt=test_prompt,
            api_url=base_url + "/stop_profile",
            prompt_len=test_prompt_len,
            output_len=test_output_len,
            logprobs=logprobs,
        )
1078
1079
1080
        profile_output = await request_func(
            request_func_input=profile_input, session=session
        )
1081
1082
        if profile_output.success:
            print("Profiler stopped")
1083
1084

    await session.close()
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
    return result


def check_goodput_args(args):
    # Check and parse goodput arguments
    goodput_config_dict = {}
    VALID_NAMES = ["ttft", "tpot", "e2el"]
    if args.goodput:
        goodput_config_dict = parse_goodput(args.goodput)
        for slo_name, slo_val in goodput_config_dict.items():
            if slo_name not in VALID_NAMES:
                raise ValueError(
                    f"Invalid metric name found, {slo_name}: {slo_val}. "
                    "The service level objective name should be one of "
1099
1100
                    f"{str(VALID_NAMES)}. "
                )
1101
1102
1103
1104
            if slo_val < 0:
                raise ValueError(
                    f"Invalid value found, {slo_name}: {slo_val}. "
                    "The service level objective value should be "
1105
1106
                    "non-negative."
                )
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
    return goodput_config_dict


def parse_goodput(slo_pairs):
    goodput_config_dict = {}
    try:
        for slo_pair in slo_pairs:
            slo_name, slo_val = slo_pair.split(":")
            goodput_config_dict[slo_name] = float(slo_val)
    except ValueError as err:
        raise argparse.ArgumentTypeError(
            "Invalid format found for service level objectives. "
1119
            'Specify service level objectives for goodput as "KEY:VALUE" '
1120
            "pairs, where the key is a metric name, and the value is a "
1121
1122
            "number in milliseconds."
        ) from err
1123
1124
1125
    return goodput_config_dict


1126
1127
1128
def save_to_pytorch_benchmark_format(
    args: argparse.Namespace, results: dict[str, Any], file_name: str
) -> None:
1129
    metrics = [
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
        "median_ttft_ms",
        "mean_ttft_ms",
        "std_ttft_ms",
        "p99_ttft_ms",
        "mean_tpot_ms",
        "median_tpot_ms",
        "std_tpot_ms",
        "p99_tpot_ms",
        "median_itl_ms",
        "mean_itl_ms",
        "std_itl_ms",
        "p99_itl_ms",
1142
1143
1144
1145
1146
1147
    ]
    # These raw data might be useful, but they are rather big. They can be added
    # later if needed
    ignored_metrics = ["ttfts", "itls", "generated_texts", "errors"]
    pt_records = convert_to_pytorch_benchmark_format(
        args=args,
1148
        metrics={k: [results[k]] for k in metrics if k in results},
1149
1150
        extra_info={
            k: results[k]
1151
1152
1153
1154
            for k in results
            if k not in metrics and k not in ignored_metrics
        },
    )
1155
1156
1157
1158
1159
1160
1161
    if pt_records:
        # Don't use json suffix here as we don't want CI to pick it up
        pt_file = f"{os.path.splitext(file_name)[0]}.pytorch.json"
        write_to_json(pt_file, pt_records)


def add_cli_args(parser: argparse.ArgumentParser):
1162
    add_dataset_parser(parser)
1163
1164
1165
1166
1167
    parser.add_argument(
        "--label",
        type=str,
        default=None,
        help="The label (prefix) of the benchmark results. If not specified, "
1168
        "the value of '--backend' will be used as the label.",
1169
    )
1170
1171
1172
    parser.add_argument(
        "--backend",
        type=str,
1173
1174
        default="openai",
        choices=list(ASYNC_REQUEST_FUNCS.keys()),
1175
        help="The type of backend or endpoint to use for the benchmark.",
1176
    )
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
    parser.add_argument(
        "--base-url",
        type=str,
        default=None,
        help="Server or API base url if not using http host and port.",
    )
    # Use 127.0.0.1 here instead of localhost to force the use of ipv4
    parser.add_argument("--host", type=str, default="127.0.0.1")
    parser.add_argument("--port", type=int, default=8000)
    parser.add_argument(
        "--endpoint",
        type=str,
        default="/v1/completions",
        help="API endpoint.",
    )
1192
1193
1194
1195
1196
    parser.add_argument(
        "--header",
        metavar="KEY=VALUE",
        nargs="*",
        help="Key-value pairs (e.g, --header x-additional-info=0.3.3) "
1197
1198
        "for headers to be passed with each request. These headers override "
        "per backend constants and values set via environment variable, and "
1199
        "will be overridden by other arguments (such as request ids).",
1200
    )
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
    parser.add_argument(
        "--max-concurrency",
        type=int,
        default=None,
        help="Maximum number of concurrent requests. This can be used "
        "to help simulate an environment where a higher level component "
        "is enforcing a maximum number of concurrent requests. While the "
        "--request-rate argument controls the rate at which requests are "
        "initiated, this argument will control how many are actually allowed "
        "to execute at a time. This means that when used in combination, the "
        "actual request rate may be lower than specified with --request-rate, "
1212
1213
        "if the server is not processing requests fast enough to keep up.",
    )
1214
1215
1216
1217

    parser.add_argument(
        "--model",
        type=str,
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
        required=False,
        default=None,
        help="Name of the model. If not specified, will fetch the first model "
        "from the server's /v1/models endpoint.",
    )
    parser.add_argument(
        "--input-len",
        type=int,
        default=None,
        help="General input length for datasets. Maps to dataset-specific "
        "input length arguments (e.g., --random-input-len, --sonnet-input-len). "
        "If not specified, uses dataset defaults.",
    )
    parser.add_argument(
        "--output-len",
        type=int,
        default=None,
        help="General output length for datasets. Maps to dataset-specific "
        "output length arguments (e.g., --random-output-len, --sonnet-output-len). "
        "If not specified, uses dataset defaults.",
1238
1239
1240
1241
    )
    parser.add_argument(
        "--tokenizer",
        type=str,
1242
        help="Name or path of the tokenizer, if not using the default tokenizer.",  # noqa: E501
1243
    )
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
    parser.add_argument(
        "--tokenizer-mode",
        type=str,
        default="auto",
        help="""Tokenizer mode:\n
        - "auto" will use the tokenizer from `mistral_common` for Mistral models
        if available, otherwise it will use the "hf" tokenizer.\n
        - "hf" will use the fast tokenizer if available.\n
        - "slow" will always use the slow tokenizer.\n
        - "mistral" will always use the tokenizer from `mistral_common`.\n
        - "deepseek_v32" will always use the tokenizer from `deepseek_v32`.\n
        - Other custom values can be supported via plugins.""",
    )
1257
1258
1259
1260
1261
    parser.add_argument("--use-beam-search", action="store_true")
    parser.add_argument(
        "--logprobs",
        type=int,
        default=None,
1262
1263
1264
1265
1266
1267
1268
        help=(
            "Number of logprobs-per-token to compute & return as part of "
            "the request. If unspecified, then either (1) if beam search "
            "is disabled, no logprobs are computed & a single dummy "
            "logprob is returned for each token; or (2) if beam search "
            "is enabled 1 logprob per token is computed"
        ),
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
    )
    parser.add_argument(
        "--request-rate",
        type=float,
        default=float("inf"),
        help="Number of requests per second. If this is inf, "
        "then all the requests are sent at time 0. "
        "Otherwise, we use Poisson process or gamma distribution "
        "to synthesize the request arrival times.",
    )
    parser.add_argument(
        "--burstiness",
        type=float,
        default=1.0,
        help="Burstiness factor of the request generation. "
        "Only take effect when request_rate is not inf. "
        "Default value is 1, which follows Poisson process. "
        "Otherwise, the request intervals follow a gamma distribution. "
        "A lower burstiness value (0 < burstiness < 1) results in more "
        "bursty requests. A higher burstiness value (burstiness > 1) "
        "results in a more uniform arrival of requests.",
    )
    parser.add_argument(
        "--trust-remote-code",
        action="store_true",
        help="Trust remote code from huggingface",
    )
    parser.add_argument(
        "--disable-tqdm",
        action="store_true",
        help="Specify to disable tqdm progress bar.",
    )
1301
1302
1303
1304
1305
1306
    parser.add_argument(
        "--num-warmups",
        type=int,
        default=0,
        help="Number of warmup requests.",
    )
1307
1308
1309
    parser.add_argument(
        "--profile",
        action="store_true",
1310
        help="Use vLLM Profiling. --profiler-config must be provided on the server.",
1311
1312
1313
1314
1315
1316
    )
    parser.add_argument(
        "--save-result",
        action="store_true",
        help="Specify to save benchmark results to a json file",
    )
1317
1318
1319
1320
    parser.add_argument(
        "--save-detailed",
        action="store_true",
        help="When saving the results, whether to include per request "
1321
        "information such as response, error, ttfts, tpots, etc.",
1322
1323
1324
1325
1326
1327
    )
    parser.add_argument(
        "--append-result",
        action="store_true",
        help="Append the benchmark result to the existing json file.",
    )
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
    parser.add_argument(
        "--metadata",
        metavar="KEY=VALUE",
        nargs="*",
        help="Key-value pairs (e.g, --metadata version=0.3.3 tp=1) "
        "for metadata of this run to be saved in the result JSON file "
        "for record keeping purposes.",
    )
    parser.add_argument(
        "--result-dir",
        type=str,
        default=None,
        help="Specify directory to save benchmark json results."
        "If not specified, results are saved in the current directory.",
    )
    parser.add_argument(
        "--result-filename",
        type=str,
        default=None,
        help="Specify the filename to save benchmark json results."
        "If not specified, results will be saved in "
        "{label}-{args.request_rate}qps-{base_model_id}-{current_dt}.json"  # noqa
        " format.",
    )
    parser.add_argument(
        "--ignore-eos",
        action="store_true",
        help="Set ignore_eos flag when sending the benchmark request."
1356
1357
        "Warning: ignore_eos is not supported in deepspeed_mii and tgi.",
    )
1358
1359
1360
    parser.add_argument(
        "--percentile-metrics",
        type=str,
1361
        default=None,
1362
        help="Comma-separated list of selected metrics to report percentiles. "
1363
        "This argument specifies the metrics to report percentiles. "
1364
1365
1366
        'Allowed metric names are "ttft", "tpot", "itl", "e2el". '
        'If not specified, defaults to "ttft,tpot,itl" for generative models '
        'and "e2el" for pooling models.',
1367
    )
1368
1369
1370
1371
    parser.add_argument(
        "--metric-percentiles",
        type=str,
        default="99",
1372
        help="Comma-separated list of percentiles for selected metrics. "
1373
1374
1375
        'To report 25-th, 50-th, and 75-th percentiles, use "25,50,75". '
        'Default value is "99".'
        'Use "--percentile-metrics" to select metrics.',
1376
1377
1378
1379
1380
    )
    parser.add_argument(
        "--goodput",
        nargs="+",
        required=False,
1381
        help='Specify service level objectives for goodput as "KEY:VALUE" '
1382
        "pairs, where the key is a metric name, and the value is in "
1383
        'milliseconds. Multiple "KEY:VALUE" pairs can be provided, '
1384
        "separated by spaces. Allowed request level metric names are "
1385
        '"ttft", "tpot", "e2el". For more context on the definition of '
1386
        "goodput, refer to DistServe paper: https://arxiv.org/pdf/2401.09670 "
1387
1388
        "and the blog: https://hao-ai-lab.github.io/blogs/distserve",
    )
1389
1390
1391
1392
    parser.add_argument(
        "--request-id-prefix",
        type=str,
        required=False,
1393
        default=f"bench-{uuid.uuid4().hex[:8]}-",
1394
1395
1396
        help="Specify the prefix of request id.",
    )

1397
1398
1399
1400
1401
    sampling_group = parser.add_argument_group("sampling parameters")
    sampling_group.add_argument(
        "--top-p",
        type=float,
        default=None,
1402
        help="Top-p sampling parameter. Only has effect on openai-compatible backends.",
1403
1404
1405
1406
1407
    )
    sampling_group.add_argument(
        "--top-k",
        type=int,
        default=None,
1408
        help="Top-k sampling parameter. Only has effect on openai-compatible backends.",
1409
1410
1411
1412
1413
    )
    sampling_group.add_argument(
        "--min-p",
        type=float,
        default=None,
1414
        help="Min-p sampling parameter. Only has effect on openai-compatible backends.",
1415
1416
1417
1418
1419
1420
1421
1422
1423
    )
    sampling_group.add_argument(
        "--temperature",
        type=float,
        default=None,
        help="Temperature sampling parameter. Only has effect on "
        "openai-compatible backends. If not specified, default to greedy "
        "decoding (i.e. temperature==0.0).",
    )
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
    sampling_group.add_argument(
        "--frequency-penalty",
        type=float,
        default=None,
        help="Frequency penalty sampling parameter. Only has effect on "
        "openai-compatible backends.",
    )
    sampling_group.add_argument(
        "--presence-penalty",
        type=float,
        default=None,
        help="Presence penalty sampling parameter. Only has effect on "
        "openai-compatible backends.",
    )
    sampling_group.add_argument(
        "--repetition-penalty",
        type=float,
        default=None,
        help="Repetition penalty sampling parameter. Only has effect on "
        "openai-compatible backends.",
    )
1445

1446
1447
1448
1449
1450
1451
    parser.add_argument(
        "--served-model-name",
        type=str,
        default=None,
        help="The model name used in the API. "
        "If not specified, the model name will be the "
1452
        "same as the `--model` argument. ",
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
    )

    parser.add_argument(
        "--lora-modules",
        nargs="+",
        default=None,
        help="A subset of LoRA module names passed in when "
        "launching the server. For each request, the "
        "script chooses a LoRA module at random.",
    )
1463

1464
1465
1466
1467
1468
1469
1470
1471
    parser.add_argument(
        "--ramp-up-strategy",
        type=str,
        default=None,
        choices=["linear", "exponential"],
        help="The ramp-up strategy. This would be used to "
        "ramp up the request rate from initial RPS to final "
        "RPS rate (specified by --ramp-up-start-rps and "
1472
1473
        "--ramp-up-end-rps.) over the duration of the benchmark.",
    )
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
    parser.add_argument(
        "--ramp-up-start-rps",
        type=int,
        default=None,
        help="The starting request rate for ramp-up (RPS). "
        "Needs to be specified when --ramp-up-strategy is used.",
    )
    parser.add_argument(
        "--ramp-up-end-rps",
        type=int,
        default=None,
        help="The ending request rate for ramp-up (RPS). "
        "Needs to be specified when --ramp-up-strategy is used.",
    )
1488
1489
1490
1491
1492
    parser.add_argument(
        "--ready-check-timeout-sec",
        type=int,
        default=600,
        help="Maximum time to wait for the endpoint to become ready "
1493
        "in seconds (default: 600 seconds / 10 minutes). If set to 0, "
1494
        "the ready check will be skipped.",
1495
    )
1496

1497
1498
1499
1500
1501
1502
1503
1504
1505
    parser.add_argument(
        "--extra-body",
        help="A JSON string representing extra body parameters to include "
        "in each request."
        'Example: \'{"chat_template_kwargs":{"enable_thinking":false}}\'',
        type=json.loads,
        default=None,
    )

1506

1507
1508
1509
def main(args: argparse.Namespace) -> dict[str, Any]:
    return asyncio.run(main_async(args))

1510

1511
async def main_async(args: argparse.Namespace) -> dict[str, Any]:
1512
1513
1514
1515
    print(args)
    random.seed(args.seed)
    np.random.seed(args.seed)

1516
1517
1518
1519
1520
1521
    # Validate ramp-up arguments
    if args.ramp_up_strategy is not None:
        if args.request_rate != float("inf"):
            raise ValueError(
                "When using ramp-up, do not specify --request-rate. "
                "The request rate will be controlled by ramp-up parameters. "
1522
1523
                "Please remove the --request-rate argument."
            )
1524
1525
1526
        if args.ramp_up_start_rps is None or args.ramp_up_end_rps is None:
            raise ValueError(
                "When using --ramp-up-strategy, both --ramp-up-start-rps and "
1527
1528
                "--ramp-up-end-rps must be specified"
            )
1529
1530
1531
1532
        if args.ramp_up_start_rps < 0 or args.ramp_up_end_rps < 0:
            raise ValueError("Ramp-up start and end RPS must be non-negative")
        if args.ramp_up_start_rps > args.ramp_up_end_rps:
            raise ValueError("Ramp-up start RPS must be less than end RPS")
1533
1534
        if args.ramp_up_strategy == "exponential" and args.ramp_up_start_rps == 0:
            raise ValueError("For exponential ramp-up, the start RPS cannot be 0.")
1535

1536
1537
1538
1539
1540
1541
    label = args.label

    if args.base_url is not None:
        api_url = f"{args.base_url}{args.endpoint}"
        base_url = f"{args.base_url}"
    else:
1542
1543
1544
        host_port = join_host_port(args.host, args.port)
        api_url = f"http://{host_port}{args.endpoint}"
        base_url = f"http://{host_port}"
1545

1546
1547
1548
1549
1550
1551
1552
1553
1554
    # Headers
    headers = None
    if args.header:
        headers = {}
        for item in args.header:
            if "=" in item:
                kvstring = item.split("=", 1)
                headers[kvstring[0].strip()] = kvstring[1].strip()
            else:
1555
                raise ValueError("Invalid header format. Please use KEY=VALUE format.")
1556

1557
1558
1559
    # Fetch model from server if not specified
    if args.model is None:
        print("Model not specified, fetching first model from server...")
1560
1561
        model_name, model_id = await get_first_model_from_server(base_url, headers)
        print(f"First model name: {model_name}, first model id: {model_id}")
1562
    else:
1563
        model_name = args.served_model_name
1564
1565
1566
1567
1568
        model_id = args.model

    tokenizer_id = args.tokenizer if args.tokenizer is not None else model_id
    tokenizer_mode = args.tokenizer_mode

1569
1570
1571
1572
1573
    tokenizer = get_tokenizer(
        tokenizer_id,
        tokenizer_mode=tokenizer_mode,
        trust_remote_code=args.trust_remote_code,
    )
1574

1575
1576
1577
    if args.dataset_name is None:
        raise ValueError(
            "Please specify '--dataset-name' and the corresponding "
1578
1579
            "'--dataset-path' if required."
        )
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593

    # Map general --input-len and --output-len to all dataset-specific arguments
    if args.input_len is not None:
        args.random_input_len = args.input_len
        args.sonnet_input_len = args.input_len

    if args.output_len is not None:
        args.random_output_len = args.output_len
        args.sonnet_output_len = args.output_len
        args.sharegpt_output_len = args.output_len
        args.custom_output_len = args.output_len
        args.hf_output_len = args.output_len
        args.spec_bench_output_len = args.output_len
        args.prefix_repetition_output_len = args.output_len
1594

1595
1596
1597
1598
1599
1600
1601
1602
    # when using random datasets, default to ignoring EOS
    # so generation runs to the requested length
    if (
        args.dataset_name in ("random", "random-mm")
        and args.backend in OPENAI_COMPATIBLE_BACKENDS
    ):
        args.ignore_eos = True

1603
1604
    # Load the dataset.
    input_requests = get_samples(args, tokenizer)
1605
1606
    goodput_config_dict = check_goodput_args(args)

1607
    backend = args.backend
1608
1609
1610
1611
1612
    task_type = (
        TaskType.POOLING
        if "embeddings" in backend or "rerank" in backend
        else TaskType.GENERATION
    )
1613

1614
    # Collect the sampling parameters.
1615
1616
1617
1618
1619
1620
1621
1622
1623
1624
1625
1626
1627
1628
1629
1630
1631
1632
1633
1634
    if task_type == TaskType.GENERATION:
        sampling_params = {
            k: v
            for k, v in {
                "top_p": args.top_p,
                "top_k": args.top_k,
                "min_p": args.min_p,
                "temperature": args.temperature,
                "frequency_penalty": args.frequency_penalty,
                "presence_penalty": args.presence_penalty,
                "repetition_penalty": args.repetition_penalty,
            }.items()
            if v is not None
        }

        # Sampling parameters are only supported by openai-compatible backend.
        if sampling_params and args.backend not in OPENAI_COMPATIBLE_BACKENDS:
            raise ValueError(
                "Sampling parameters are only supported by openai-compatible backends."
            )
1635

1636
1637
        if "temperature" not in sampling_params:
            sampling_params["temperature"] = 0.0  # Default to greedy decoding.
1638
1639

        default_percentile_metrics = "ttft,tpot,itl"
1640
1641
    else:
        sampling_params = {}
1642
        default_percentile_metrics = "e2el"
1643

1644
1645
1646
    extra_body = args.extra_body or {}
    extra_body = {**sampling_params, **extra_body}

1647
1648
    percentile_metrics: str = args.percentile_metrics or default_percentile_metrics

1649
    # Avoid GC processing "static" data - reduce pause times.
1650
    freeze_gc_heap()
1651

1652
    benchmark_result = await benchmark(
1653
1654
        task_type=task_type,
        endpoint_type=backend,
1655
1656
1657
1658
1659
1660
1661
1662
1663
1664
        api_url=api_url,
        base_url=base_url,
        model_id=model_id,
        model_name=model_name,
        tokenizer=tokenizer,
        input_requests=input_requests,
        logprobs=args.logprobs,
        request_rate=args.request_rate,
        burstiness=args.burstiness,
        disable_tqdm=args.disable_tqdm,
1665
        num_warmups=args.num_warmups,
1666
        profile=args.profile,
1667
        selected_percentile_metrics=percentile_metrics.split(","),
1668
        selected_percentiles=[float(p) for p in args.metric_percentiles.split(",")],
1669
1670
1671
1672
        ignore_eos=args.ignore_eos,
        goodput_config_dict=goodput_config_dict,
        max_concurrency=args.max_concurrency,
        lora_modules=args.lora_modules,
1673
        extra_headers=headers,
1674
        extra_body=extra_body,
1675
1676
1677
1678
1679
        ramp_up_strategy=args.ramp_up_strategy,
        ramp_up_start_rps=args.ramp_up_start_rps,
        ramp_up_end_rps=args.ramp_up_end_rps,
        ready_check_timeout_sec=args.ready_check_timeout_sec,
    )
1680
1681

    # Save config and results to json
1682
1683
1684
1685
1686
    result_json: dict[str, Any] = {}

    # Setup
    current_dt = datetime.now().strftime("%Y%m%d-%H%M%S")
    result_json["date"] = current_dt
1687
    result_json["endpoint_type"] = args.backend  # for backward compatibility
1688
    result_json["backend"] = args.backend
1689
1690
1691
1692
1693
1694
1695
1696
1697
    result_json["label"] = label
    result_json["model_id"] = model_id
    result_json["tokenizer_id"] = tokenizer_id
    result_json["num_prompts"] = args.num_prompts

    # Metadata
    if args.metadata:
        for item in args.metadata:
            if "=" in item:
1698
                kvstring = item.split("=", 1)
1699
1700
1701
                result_json[kvstring[0].strip()] = kvstring[1].strip()
            else:
                raise ValueError(
1702
1703
                    "Invalid metadata format. Please use KEY=VALUE format."
                )
1704

1705
    # Traffic
1706
1707
1708
    result_json["request_rate"] = (
        args.request_rate if args.request_rate < float("inf") else "inf"
    )
1709
1710
1711
1712
1713
1714
1715
1716
1717
1718
1719
1720
1721
1722
    result_json["burstiness"] = args.burstiness
    result_json["max_concurrency"] = args.max_concurrency

    if args.ramp_up_strategy is not None:
        result_json["ramp_up_strategy"] = args.ramp_up_strategy
        result_json["ramp_up_start_rps"] = args.ramp_up_start_rps
        result_json["ramp_up_end_rps"] = args.ramp_up_end_rps

    # Merge with benchmark result
    result_json = {**result_json, **benchmark_result}

    if not args.save_detailed:
        # Remove fields with too many data points
        for field in [
1723
1724
1725
1726
1727
1728
            "input_lens",
            "output_lens",
            "ttfts",
            "itls",
            "generated_texts",
            "errors",
1729
1730
1731
1732
1733
        ]:
            if field in result_json:
                del result_json[field]
            if field in benchmark_result:
                del benchmark_result[field]
1734

1735
        # Save to file
1736
    if args.save_result or args.append_result:
1737
        base_model_id = model_id.split("/")[-1]
1738
1739
1740
1741
1742
        max_concurrency_str = (
            f"-concurrency{args.max_concurrency}"
            if args.max_concurrency is not None
            else ""
        )
1743
        label = label or args.backend
1744
        if args.ramp_up_strategy is not None:
1745
            file_name = f"{label}-ramp-up-{args.ramp_up_strategy}-{args.ramp_up_start_rps}qps-{args.ramp_up_end_rps}qps{max_concurrency_str}-{base_model_id}-{current_dt}.json"  # noqa
1746
1747
        else:
            file_name = f"{label}-{args.request_rate}qps{max_concurrency_str}-{base_model_id}-{current_dt}.json"  # noqa
1748
1749
1750
        if args.result_filename:
            file_name = args.result_filename
        if args.result_dir:
1751
            os.makedirs(args.result_dir, exist_ok=True)
1752
            file_name = os.path.join(args.result_dir, file_name)
1753
1754
1755
        with open(
            file_name, mode="a+" if args.append_result else "w", encoding="utf-8"
        ) as outfile:
1756
1757
1758
            # Append a newline.
            if args.append_result and outfile.tell() != 0:
                outfile.write("\n")
1759
1760
            json.dump(result_json, outfile)
        save_to_pytorch_benchmark_format(args, result_json, file_name)
1761

1762
    return result_json